https://github.com/sandialabs/prime

PRIME is a modeling framework designed for the "real-time" characterization and forecasting of partially observed epidemics
https://github.com/sandialabs/prime

scr-2595 snl-data-analysis snl-science-libs

Last synced: 5 months ago
JSON representation

PRIME is a modeling framework designed for the "real-time" characterization and forecasting of partially observed epidemics

• Host: GitHub
• URL: https://github.com/sandialabs/prime
• Owner: sandialabs
• License: bsd-2-clause
• Created: 2021-02-10T01:11:20.000Z (over 5 years ago)
• Default Branch: main
• Last Pushed: 2021-03-13T16:40:57.000Z (over 5 years ago)
• Last Synced: 2025-05-02T04:35:12.245Z (about 1 year ago)
• Topics: scr-2595, snl-data-analysis, snl-science-libs
• Language: Python
• Homepage: http://sandialabs.github.io/PRIME
• Size: 8.96 MB
• Stars: 9
• Watchers: 7
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
![name-of-you-image](https://github.com/sandialabs/PRIME/blob/main/logo/PRIME_logo_small.jpg?raw=true)

PRIME is a modeling framework designed for the “real-time” characterization and forecasting of partially observed epidemics. Characterization is the estimation of infection spread parameters using daily counts of symptomatic pa- tients. The method is designed to help guide medical resource allocation in the early epoch of the outbreak. The estimation problem is posed as one of Bayesian inference and solved using a Markov Chain Monte Carlo technique. The framework can accommodate multiple epidemic waves and can help identify different disease dynamics at the regional, state, and country levels. We include examples using publicly available COVID-19 data.

PRIME was written and developed by Cosmin Safta, Jaideep Ray, Patrick Blonigan, and Kamaljit Chowdhary. We would like to acknowledge helpful suggestions made by several colleagues at Sandia National Laboratories: Erin Acquesta, Thomas Catanach, Bert Debusschere, Sean DeRosa, Pat Finley, Edgar Galvan, Gianluca Geraci, John D. Jakeman, Mohammad Khalil, Khachik Sargsyan, and Teresa Portone.

## Citing:

* C. Safta, K. Sargsyan, J. Ray, "Characterization of Partially Observed Epidemics through Bayesian Inference - Application to COVID-19", Computational Mechanics, doi: 10.1007/s00466-020-01897-z (2020).